Using prior information on the intraclass correlation coefficient to analyze data from unreplicated and under-replicated experiments
There often is an abundance of subsampling to estimate the within unit component of
variance, but what is needed for statistical tests is an estimate of the between unit component
of variance. There is evidence to suggest that the ratio of the between component of variance
to the total variance will remain relatively constant over a range of studies of similar types.
Moreover, in many cases this intraclass correlation, which is the ratio of the between unit variance to the total variance, will be relatively small, often 0.1 or less. Such situations exist in education, agriculture, and medicine to name a few.
The present study discusses how to use such prior information on the intraclass correlation
coefficient (ICC) to obtain inferences about differences among treatments in the face of no
replication. Several strategies that use the ICC are recommended for different situations and
various designs. Their properties are investigated. Work is extended to under-replicated
experiments. The work has a Bayesian flavor but avoids the full Bayesian analysis, which has
computational complexities and the potential for lack of acceptance among many applied
researchers. This study compares the prior information ICC methods with traditional
methods. Situations are suggested in which prior information ICC methods are preferable to
traditional methods and those in which the traditional methods are preferable.
School:Kansas State University
School Location:USA - Kansas
Source Type:Master's Thesis
Keywords:intraclass correlation coefficient unreplicated experiments under replicated prior information unit of analysis group means statistics 0463 agriculture general 0473 education 0515
Date of Publication:01/01/2004